Existing algorithmic approaches towards imitation learning can be divided into two groups: behavioral cloning (BC) and inverse reinforcement learning (IRL) or inverse optimal control (IOC), both can be further subdivided into model-based and model-free approaches depending on whether the system dynamics is available or not [1]}. BC and IRL make different assumptions about the correspondence of learner and expert. In BC, a mapping from states to actions
is generated from the demonstrations using supervised learning methods. This mapping can then be used by the learner to reproduce similar behavior, provided that the embodiments of expert and learner are alike, otherwise the method will fail due to lack of correspondence.
